A Refinement on the Principle of Resistance: The Puritan Roots of Political Resistance in America

Puritanism was a religious movement that historically developed with an innate tendency toward political resistance. Birthed out of the complexities of the English Reformation, Puritan non-conformity caused tensions between dissenters and the English monarchs. These tensions followed non-conformists when they chose to emigrate to Massachusetts Bay in order to establish a church and government favorable to their ideas of Congregationalism. Their experience in New England continued to demonstrate the Puritan penchant toward political resistance as they strove to develop and maintain a virtual independent, sovereign republic despite attempts by the royal government to bring the Northern colonies into conformity consistent with imperial colonial policies. The structure of government imposed upon Massachusetts with the second charter emphasized and enhanced the political divisions that had grown within the colony. These divisions developed into rebellious tendencies directed against the loyalist component of the colonial government and the royal government in Great Britain that eventually built up to the open hostilities of the American Revolution under the influence of the Puritan clergy of New England. This dissertation traces the persistence of Puritan political resistance and argues that it was a result of the history of English dissenters that produced and maintained it as a characteristic of Puritanism both in England and America and was one of the reasons why revolutionary hostilities of the latter half of the eighteenth century began first in New England

Forward Modeling of Double Neutron Stars: Insights from Highly-Offset Short Gamma-Ray Bursts

We present a detailed analysis of two well-localized, highly offset short gamma-ray bursts---GRB~070809 and GRB~090515---investigating the kinematic evolution of their progenitors from compact object formation until merger. Calibrating to observations of their most probable host galaxies, we construct semi-analytic galactic models that account for star formation history and galaxy growth over time. We pair detailed kinematic evolution with compact binary population modeling to infer viable post-supernova velocities and inspiral times. By populating binary tracers according to the star formation history of the host and kinematically evolving their post-supernova trajectories through the time-dependent galactic potential, we find that systems matching the observed offsets of the bursts require post-supernova systemic velocities of hundreds of kilometers per second. Marginalizing over uncertainties in the stellar mass--halo mass relation, we find that the second-born neutron star in the GRB~070809 and GRB~090515 progenitor systems received a natal kick of $\gtrsim 200~\mathrm{km\,s}^{-1}$ at the 78\% and 91\% credible levels, respectively. Applying our analysis to the full catalog of localized short gamma-ray bursts will provide unique constraints on their progenitors and help unravel the selection effects inherent to observing transients that are highly offset with respect to their hosts.Comment: 18 pages, 7 figures, 1 table. ApJ, in pres

Within- and across-breed imputation of high-density genotypes in dairy and beef cattle from medium- and low-density genotypes

peer-reviewedFinancial support of the Irish Department of Agriculture Research Stimulus Fund (RSF-06-0353; RSF-06-0428; 11/SF/311), Science Foundation Ireland (09/IN.1/B2642) and the Irish dairy and beef industry are gratefully acknowledged.The objective of this study was to evaluate, using three different genotype density panels, the accuracy of imputation from lower- to higher-density genotypes in dairy and beef cattle. High-density genotypes consisting of 777 962 single-nucleotide polymorphisms (SNP) were available on 3122 animals comprised of 269, 196, 710, 234, 719, 730 and 264 Angus, Belgian Blue, Charolais, Hereford, Holstein-Friesian, Limousin and Simmental bulls, respectively. Three different genotype densities were generated: low density (LD; 6501 autosomal SNPs), medium density (50K; 47 770 autosomal SNPs) and high density (HD; 735 151 autosomal SNPs). Imputation from lower- to higher-density genotype platforms was undertaken within and across breeds exploiting population-wide linkage disequilibrium. The mean allele concordance rate per breed from LD to HD when undertaken using a single breed or multiple breed reference population varied from 0.956 to 0.974 and from 0.947 to 0.967, respectively. The mean allele concordance rate per breed from 50K to HD when undertaken using a single breed or multiple breed reference population varied from 0.987 to 0.994 and from 0.987 to 0.993, respectively. The accuracy of imputation was generally greater when the reference population was solely comprised of the breed to be imputed compared to when the reference population comprised of multiple breeds, although the impactDepartment of Agriculture, Food and the MarineScience Foundation Irelan

Imputation of ungenotyped parental genotypes in dairy and beef cattle from progeny genotypes

peer-reviewedThe objective of this study was to quantify the accuracy of imputing the genotype of parents using information on the genotype of their progeny and a family-based and population-based imputation algorithm. Two separate data sets were used, one containing both dairy and beef animals (n = 3122) with high-density genotypes (735 151 single nucleotide polymorphisms (SNPs)) and the other containing just dairy animals (n = 5489) with medium-density genotypes (51 602 SNPs). Imputation accuracy of three different genotype density panels were evaluated representing low (i.e. 6501 SNPs), medium and high density. The full genotypes of sires with genotyped half-sib progeny were masked and subsequently imputed. Genotyped half-sib progeny group sizes were altered from 4 up to 12 and the impact on imputation accuracy was quantified. Up to 157 and 258 sires were used to test the accuracy of imputation in the dairy plus beef data set and the dairy-only data set, respectively. The efficiency and accuracy of imputation was quantified as the proportion of genotypes that could not be imputed, and as both the genotype concordance rate and allele concordance rate. The median proportion of genotypes per animal that could not be imputed in the imputation process decreased as the number of genotyped half-sib progeny increased; values for the medium-density panel ranged from a median of 0.015 with a half-sib progeny group size of 4 to a median of 0.0014 to 0.0015 with a half-sib progeny group size of 8. The accuracy of imputation across different paternal half-sib progeny group sizes was similar in both data sets. Concordance rates increased considerably as the number of genotyped half-sib progeny increased from four (mean animal allele concordance rate of 0.94 in both data sets for the medium-density genotype panel) to five (mean animal allele concordance rate of 0.96 in both data sets for the medium-density genotype panel) after which it was relatively stable up to a half-sib progeny group size of eight. In the data set with dairy-only animals, sufficient sires with paternal half-sib progeny groups up to 12 were available and the withinanimal mean genotype concordance rates continued to increase up to this group size. The accuracy of imputation was worst for the low-density genotypes, especially with smaller half-sib progeny group sizes but the difference in imputation accuracy between density panels diminished as progeny group size increased; the difference between high and medium-density genotype panels was relatively small across all half-sib progeny group sizes. Where biological material or genotypes are not available on individual animals, at least five progeny can be genotyped (on either a medium or high-density genotyping platform) and the parental alleles imputed with, on average, ⩾96% accuracy

First-Pass Meconium Samples from Healthy Term Vaginally-Delivered Neonates : An Analysis of the Microbiota

Acknowledgments The authors would like to thank the parents who consented to provide samples with limited notice at an emotional and stressful time. This work was supported entirely from personal donations to the neonatal endowments fund at Aberdeen Maternity Hospital and we thank families for their continued generosity, year-on-year. The Rowett Institute of Nutrition and Health receives funding from the Scottish Government (SG-RESAS). Funding: This work was funded from NHS Grampian Neonatal Endowments. The Rowett Institute receives funding from the Rural and Environmental Science and Analytical Services programme of the Scottish Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

A Human-Systems Approach to Proactively Managing Risk through Training in an Evolving Aviation Industry

The Aviation industry is rapidly evolving through increased automation on the flight deck, new air traffic control tools and procedures, and expanded applications of Unmanned Aircraft Systems (UAS). The majority of these enhancements will rely on human operators (pilots, air traffic controllers, dispatchers, etc.) in order to be safely integrated into the National Airspace System. The staggered development cycle of these technological changes, coupled with independent development teams and relatively limited operational testing opportunities, can create significant challenges. These technological enhancements must be met with similarly rapid advancements in risk mitigation and training. In this presentation we describe a standardized approach to proactively identify and assess the potential human error modes and conditions for new or proposed technological or procedural changes in the context of NAS operations. The Human-Organization and Safety Technique (HOST) is designed to examine a system or tool with the goal of improving human performance during the design stages by mitigating opportunities for human error. Human error in complex systems is rarely the result of a single error but stems from the complex interactions of multiple factors and natural performance variability. Results of a HOST analysis outline critical human-human and human-system interactions and describe and prioritize potential human performance hazards associated with each interaction. The resulting models and human performance hazards provide a comprehensive roadmap for the development of new human factors-focused training programs to ensure that pilots, air traffic controllers, and maintenance personnel are prepared for the changes and have the best opportunity to avoid error and mitigate risk in the future

Dynamic and Adaptive Training for Enhanced Aviation Knowledge Transfer and Retention

The world of aviation is rapidly evolving through increased automation on the flight deck, new air traffic control tools and procedures, and expanded applications of Unmanned Aircraft Systems (UAS). These enhancements may increase training requirements on operational personnel and potentially introduce the opportunity for the degradation of knowledge, skills, and abilities (KSAs) that are not routinely applied. The resources required for simulator-based training results in using computer-based training (CBT) for many infrequently used KSAs. Field studies and academic literature consistently show that users find this training boring, easily forgettable, and are perceived as “check the box” training. Furthermore, most CBT is standardized and does not adjust to the trainee’s learning preferences or existing familiarity with the content. In this presentation, we describe a new approach to training delivery. Our Dynamic Adaptive Training & Evaluation System (DATES) approach is designed to increase engagement, long-term retention, and decrease training time by adjusting to trainees’ learning preferences and proficiency levels. DATES presents training material in different formats and orders based on trainee performance on embedded assessments and real-time analysis of user engagement. The system starts by administering a pre-test on the topic and then presents a random order of short, individual modules in visual (video-based), verbal (text-based), or scenario-based formats. Based on response time on embedded assessments, question response accuracy, and proprietary user-engagement metrics, the system’s algorithm will present tailored training styles and modalities to maximize impact for individual trainees. We will discuss the key considerations and implementation recommendations

You Can't Always Get What You Want: The Impact of Prior Assumptions on Interpreting GW190412

GW190412 is the first observation of a black hole binary with definitively unequal masses. GW190412's mass asymmetry, along with the measured positive effective inspiral spin, allowed for inference of a component black hole spin: the primary black hole in the system was found to have a dimensionless spin magnitude between 0.17 and 0.59 (90% credible range). We investigate how the choice of priors for the spin magnitudes and tilts of the component black holes affect the robustness of parameter estimates for GW190412, and report Bayes factors across a suite of prior assumptions. Depending on the waveform family used to describe the signal, we find either marginal to moderate (2:1-6:1) or strong ($\gtrsim$ 20:1) support for the primary black hole being spinning compared to cases where only the secondary is allowed to have spin. We show how these choices influence parameter estimates, and find the asymmetric masses and positive effective inspiral spin of GW190412 to be qualitatively, but not quantitatively, robust to prior assumptions. Our results highlight the importance of both considering astrophysically motivated or population-based priors in interpreting observations and considering their relative support from the data.Comment: 12 pages, 2 figures, 1 table, published in ApJ

You Can Always Get What You Want: The Impact of Prior Assumptions on Interpreting GW190412

GW190412 is the first observation of a black hole binary with definitively unequal masses. GW190412's mass asymmetry, along with the measured positive effective inspiral spin, allowed for inference of a component black hole spin: the primary black hole in the system was found to have a dimensionless spin magnitude between 0.17 and 0.59 (90% credible range). We investigate how the choice of priors for the spin magnitudes and tilts of the component black holes affect the robustness of parameter estimates for GW190412, and report Bayes factors across a suite of prior assumptions. Depending on the waveform family used to describe the signal, we find either marginal to moderate (2:1-7:1) or strong (≳ 20:1) support for the primary black hole being spinning compared to cases where only the secondary is allowed to have spin. We show how these choices influence parameter estimates, and find the asymmetric masses and positive effective inspiral spin of GW190412 to be qualitatively, but not quantitatively, robust to prior assumptions. Our results highlight the importance of both considering astrophysically-motivated or population-based priors in interpreting observations, and considering their relative support from the data